total_travel_time = [cost_route(i) for i in route_combinations]
index = pd.MultiIndex.from_tuples(route_combinations, names=['orig', 'dest'])
df_total_travel_time = pd.Series(total_travel_time, index=index)
df_total_travel_time.drop_duplicates(inplace=True)
df_total_travel_time.head()

cost_matrix = np.zeros(shape=(len(unique_tickets), len(unique_tickets)))

for i, j in enumerate(unique_tickets):
    for k, l in enumerate(unique_tickets):

        try:
            cost_matrix[i, k] =  df_total_travel_time.loc[(j, l), ]
        except KeyError:
            cost_matrix[i, k] =  0

i_lower = np.tril_indices(len(unique_tickets), -1)
cost_matrix[i_lower] = cost_matrix.T[i_lower]
cost_matrix = cost_matrix.astype(int)
cost_matrix = np.insert(cost_matrix, 0, 0, axis=0)
cost_matrix = np.insert(cost_matrix, 0, 0, axis=1)
cost_matrix = cost_matrix.tolist()
cost_matrix

from ortools.constraint_solver import routing_enums_pb2
from ortools.constraint_solver import pywrapcp


def print_solution(data, manager, routing, solution):
    """Prints solution on console."""
    print(f'Objective: {solution.ObjectiveValue()}')
    time_dimension = routing.GetDimensionOrDie('Time')
    total_time = 0
    for engineer_id in range(data['num_engineers']):
        index = routing.Start(engineer_id)
        plan_output = 'Route for engineer {}:\n'.format(engineer_id)
        while not routing.IsEnd(index):
            time_var = time_dimension.CumulVar(index)
            plan_output += '{0} Time({1},{2}) -> '.format(
                manager.IndexToNode(index), solution.Min(time_var),
                solution.Max(time_var))
            index = solution.Value(routing.NextVar(index))
        time_var = time_dimension.CumulVar(index)
        plan_output += '{0} Time({1},{2})\n'.format(manager.IndexToNode(index),
                                                    solution.Min(time_var),
                                                    solution.Max(time_var))
        plan_output += 'Time of the route: {}min\n'.format(
            solution.Min(time_var))
        print(plan_output)
        total_time += solution.Min(time_var)
    print('Total time of all routes: {}min'.format(total_time))


def main():
    """Solve the VRP with time windows."""
    # Instantiate the data problem.
    data = create_data_model()

    # Create the routing index manager.
    manager = pywrapcp.RoutingIndexManager(len(data['time_matrix']),
                                           data['num_engineers'],
                                           data['starts'],
                                           data['ends'])

    # Create Routing Model.
    routing = pywrapcp.RoutingModel(manager)

    # Create and register a transit callback.
    def time_callback(from_index, to_index):
        """Returns the travel time between the two nodes."""
        # Convert from routing variable Index to time matrix NodeIndex.
        from_node = manager.IndexToNode(from_index)
        to_node = manager.IndexToNode(to_index)
        return data['time_matrix'][from_node][to_node]

    transit_callback_index = routing.RegisterTransitCallback(time_callback)

    # Define cost of each arc.
    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)

    # Add Time Windows constraint.
    time = 'Time'
    routing.AddDimension(
        transit_callback_index,
        30,  # allow waiting time
        400,  # maximum time per engineer
        False,  # Don't force start cumul to zero.
        time)
    time_dimension = routing.GetDimensionOrDie(time)
    # Add time window constraints for each location except start locations.
    starts = data['starts'] + [0]
    for location_idx, time_window in enumerate(data['time_windows']):
        if location_idx in starts:
            continue
        index = manager.NodeToIndex(location_idx)
        time_dimension.CumulVar(index).SetRange(time_window[0], time_window[1])

    # Instantiate route start and end times to produce feasible times.
    for i in range(data['num_engineers']):
        routing.AddVariableMinimizedByFinalizer(
            time_dimension.CumulVar(routing.Start(i)))
        routing.AddVariableMinimizedByFinalizer(
            time_dimension.CumulVar(routing.End(i)))

    # Setting first solution heuristic.
    search_parameters = pywrapcp.DefaultRoutingSearchParameters()
    search_parameters.first_solution_strategy = (
        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)

    # Solve the problem.
    solution = routing.SolveWithParameters(search_parameters)

    # Print solution on console.
    if solution:
        print_solution(data, manager, routing, solution)


if __name__ == '__main__':
    main()

from ortools.constraint_solver import routing_enums_pb2
from ortools.constraint_solver import pywrapcp


def print_solution(data, manager, routing, solution):
    """Prints solution on console."""
    print(f'Objective: {solution.ObjectiveValue()}')
    time_dimension = routing.GetDimensionOrDie('Time')
    total_time = 0
    for engineer_id in range(data['num_engineers']):
        index = routing.Start(engineer_id)
        plan_output = 'Route for engineer {}:\n'.format(engineer_id)
        while not routing.IsEnd(index):
            time_var = time_dimension.CumulVar(index)
            plan_output += '{0} Time({1},{2}) -> '.format(
                manager.IndexToNode(index), solution.Min(time_var),
                solution.Max(time_var))
            index = solution.Value(routing.NextVar(index))
        time_var = time_dimension.CumulVar(index)
        plan_output += '{0} Time({1},{2})\n'.format(manager.IndexToNode(index),
                                                    solution.Min(time_var),
                                                    solution.Max(time_var))
        plan_output += 'Time of the route: {}min\n'.format(
            solution.Min(time_var))
        print(plan_output)
        total_time += solution.Min(time_var)
    print('Total time of all routes: {}min'.format(total_time))


def main():
    """Solve the VRP with time windows."""
    # Instantiate the data problem.
    data = create_data_model()

    # Create the routing index manager.
    manager = pywrapcp.RoutingIndexManager(len(data['time_matrix']),
                                           data['num_engineers'],
                                           data['starts'],
                                           data['ends'])

    # Create Routing Model.
    routing = pywrapcp.RoutingModel(manager)

    # Create and register a transit callback.
    def time_callback(from_index, to_index):
        """Returns the travel time between the two nodes."""
        # Convert from routing variable Index to time matrix NodeIndex.
        from_node = manager.IndexToNode(from_index)
        to_node = manager.IndexToNode(to_index)
        return data['time_matrix'][from_node][to_node]

    transit_callback_index = routing.RegisterTransitCallback(time_callback)

    # Define cost of each arc.
    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)

    # Add Time Windows constraint.
    time = 'Time'
    routing.AddDimension(
        transit_callback_index,
        30,  # allow waiting time
        400,  # maximum time per engineer
        False,  # Don't force start cumul to zero.
        time)
    time_dimension = routing.GetDimensionOrDie(time)
    # Add time window constraints for each location except start locations.
    starts = data['starts'] + [0]
    for location_idx, time_window in enumerate(data['time_windows']):
        if location_idx in starts:
            continue
        index = manager.NodeToIndex(location_idx)
        time_dimension.CumulVar(index).SetRange(time_window[0], time_window[1])

    # Instantiate route start and end times to produce feasible times.
    for i in range(data['num_engineers']):
        routing.AddVariableMinimizedByFinalizer(
            time_dimension.CumulVar(routing.Start(i)))
        routing.AddVariableMinimizedByFinalizer(
            time_dimension.CumulVar(routing.End(i)))

    # Setting first solution heuristic.
    search_parameters = pywrapcp.DefaultRoutingSearchParameters()
    search_parameters.first_solution_strategy = (
        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)

    # Constraint to skills (set engineers to each ticket)
    for ticket, engineers in enumerate(data['skill'], start=1):
        engineers.insert(0, -1)
        routing.VehicleVar(manager.NodeToIndex(ticket)).SetValues(engineers)

    # Solve the problem.
    solution = routing.SolveWithParameters(search_parameters)

    # Print solution on console.
    if solution:
        print_solution(data, manager, routing, solution)


if __name__ == '__main__':
    main()